Method for the production of a burner unit

ABSTRACT

In the case of swirl-stabilized premix burners ( 1 ), an axial mass flow distribution of the fuel introduced which has especially favorable values with respect to characteristics such as NO x  emission and maximum amplitudes of pulsations occurring is used. For this purpose, Pareto solutions are determined with respect to the said characteristics, in that a distributing device ( 5 ) with control valves is represented by a tree structure with distributing parameters, and values for the distributing parameters on the basis of which the distributing device ( 5 ) is set by means of a control unit ( 10 ) are iteratively generated in a data-processing system ( 9 ) by an evolutionary algorithm. On the basis of the values determined by a measuring unit ( 11 ), solutions which are especially favorable with respect to the characteristics mentioned, espectially Pareto-optimal, are selected. The distributing devices or the premix burners of the burner system are then formed in a way corresponding to such a solution.

This is a national stage application under 35 U.S.C. § 371 ofInternational application number PCT/IB02/00282, filed 30 Jan. 2002, andclaims priority therethrough under 35 U.S.C. § 119 to German applicationnumber 101 04 151.9, filed 30 Jan. 2001.

TECHNICAL FIELD

The invention relates to a method for producing a burner system of thetype used in gas turbines.

PRIOR ART

It is known that burner systems of the generic type, with customaryswirl-stabilized premix burners, in which the fuel is introduced usuallymore or less uniformly over the length, have problematicalcharacteristics in various respects to do with the way in which thecombustion proceeds. In particular, the exhaust gases often contain aconsiderable proportion of pollutants, especially NO_(x). Pressure wavesinduced by pulsating combustion also often present difficulties, sincethey subject the gas turbine to high mechanical loads and reduce itsservice life.

To alleviate these problems, it has been proposed to stabilize thecombustion by influencing the pressure in the burner system by means offeedback. For this purpose, in that case the pressure was measured andthe measured signal fed in again in a phase-shifted manner vialoudspeakers. In this way it was possible to achieve a more stablecombustion and, as a result, a reduction in the formation of pressurewaves and also the NO_(x) and CO emissions. See in this respect C. O.Paschereit, E. Gutmark, W. Weisenstein: ‘Structure and Control ofThermoacoustic Instabilities in a Gas-turbine Combustor’, Combust. Sci.and Tech. 138 (1998), pages 213–232. The required expenditure in termsof apparatus is very considerable, however.

SUMMARY OF THE INVENTION

The invention is based on the object of providing a method for producingburner systems of the generic type which are of a simple constructionand in which the combustion proceeds favorably, in particular withregard to the reduction of pulsations and low emission of pollutants,especially NO_(x). It was found that the way in which the combustionproceeds is influenced strongly by the mass flow distribution of thefuel introduced into the premix burners.

According to the invention, the burner systems are formed in such a waythat the fuel is introduced into the premix burners with a specific massflow distribution, which ensures favorable characteristics of thecombustion, especially with regard to pulsations and pollutant emission.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is explained in more detail below on the basis of figures,which merely represent an exemplary embodiment and in which

FIG. 1 schematically shows a premix burner with an upstream distributingdevice,

FIG. 2 schematically shows a setup of a test system with a premix burnercorresponding to FIG. 1 and a distributing device and also adata-processing system for determining favorable mass flowdistributions,

FIG. 3 shows a diagram of a tree structure as a simplified model for themass flow distribution;

FIGS. 4, 5 a,b generally show the optimizing method used for thedetermination of favorable mass flow distributions, where

FIG. 4 shows the determination set of a typical optimization problem andits mapping onto the corresponding target set and

FIGS. 5 a, b show steps in the selection of new determination variablesfrom previously generated test variables in the target domain;

FIGS. 6 a, b show the target domain of the present optimization problemafter 20 and 64 iteration steps, respectively,

FIG. 7 shows mass flow distributions according to selected solutions ofthe optimization problem.

WAYS OF IMPLEMENTING THE INVENTION

A premix burner 1 (FIG. 1) of a fundamentally known construction, asused in an internal combustion engine of a gas turbine, has the form ofa truncated cone with an outflow opening 2 at its wide end. Providedalong two diametrically opposite generatrices are air inlet slots 3 a,b, on the outer sides of each of which 16 inlet openings 4 for the fuelsupply are arranged, forming the end points on the burner side of adistributing device 5.

In the course of producing a burner system, firstly mass flowdistributions which are as favorable as possible with regard to a targetvariable, the components of which are formed by specificcharacteristics, especially the emission of No_(x) and the maximum ofamplitudes of pressure surges occurring, are formed. This takes place bymeans of a test setup (FIG. 2), in which a distributing device 5suitable for test purposes, which may be formed for example asrepresented in FIG. 1, is arranged upstream of a premix burner 1 formedas described in connection with FIG. 1.

The input of the distributing device 5 is formed by a feed line 6, whichis connected to a fuel source, for example a stationary gas line (notrepresented) and is provided with an input valve 7, which limits thefuel supply. Subsequently, the main line 6 branches into two branchlines 8 a,b, from each of which there branch off four supply lines, inwhich a control valve is respectively located. The control valves aredesignated by V₁ to V₈. Following the respective control valve, thesupply line branches to two pairs of inlet openings 4, lying oppositeeach other, to be precise in such a way that two axially successivegroups of four inlet openings respectively have fuel applied to them viaone of the control valves V₁, . . . , V₈. The control valves V₁, . . . ,V₈ are formed in such a way that specific mass flows m₁, . . . m₈ can beset with them. The two inlet openings 4 arranged on the same side arepreceded in each case by an on/off valve. By means of the on/off valvesV′′₁, . . . , V′′₁₆, it is possible in each case for the fuel supply totwo successive inlet openings 4 to be selectively blocked.

The construction of the distributing device 5 may deviate in manyrespects from that described. For instance, each control valve may beassigned a larger or smaller group of inlet openings or else only asingle inlet opening. The on/off valves may be inserted at a differentlocation or else be omitted, or such valves may be used exclusively, forexample one for each inlet opening. The topology may also be different,for example it may correspond to the distributing device 5′ representedin FIG. 3 (FIG. 3), a tree structure comprising three-way valves, asdescribed in more detail further below. The tests of which the resultsare given further below were carried out with a distributing devicewhich corresponded to that represented in FIG. 1, but without the on/offvalves V′′₁, . . . , V′′₁₆.

The control valves V₁, . . . , V₈ of the distributing device 5 are setby a control unit 10 on the basis of values output by thedata-processing system 9. A measuring unit 11 supplies the measuredcharacteristics of the burner system to the data-processing system 9.For the representation of the mass flow distribution in thedata-processing system 9, the distributing device 5 is mapped onto thedistributing device 5′ (FIG. 3), i.e. a model in which it is representedby a binary tree structure comprising three-way valves V′₁, . . . , V′₇is used and it is assumed that the total mass flow respectively has afixed value M. The position of each of the three-way valves can berepresented by a distributing parameter p, 0≦p≦1, which corresponds tothe proportion attributed to the left-hand output in the distribution ofthe mass flow between the left-hand and the right-hand output. If theindividual mass flows at the output of the control valves V₁, . . . , V₈are designated by m₁, . . . , m₈, the distributing parameter of thevalve V′₁ becomes p₁=(m₁+ . . . +m₄)/M, that of the valve V′₂ becomesp₂=(m₁+m₂)/(m₁+ . . . +m₄), etc., and conversely m₁, . . . , m₈ caneasily be calculated from the distributing parameters p₁, . . . , p₇ onthe basis of m₁=Mp₁p₂p₄, m₂=Mp₁p₂(1−p₄), and so on. The fact that thedata-processing system 9 works with the model described has the effectthat only seven parameters are required, and consequently the dimensionof the determination domain (see below) is reduced by 1.

If, as in the present case, optimization is carried out with regard to anumber of independent characteristics, it is generally not possible toselect a specific optimum solution, but nevertheless a set of so-calledPareto-optimal solutions can be found, respectively characterized inthat they are not Pareto-dominated, i.e. that there is no other solutionwhich would be more favorable with regard to one characteristic and noless favorable with regard to any of the other characteristics. To putit another way, a solution which is more favorable with regard to atleast one characteristic than a Pareto-optimal solution is inevitablyless favorable than the latter with regard to at least one othercharacteristic.

The target variables of the Pareto-optimal solutions usually form aportion of a hypersurface in the target domain defined by the targetvariables, known as the Pareto front, which bounds the target set, i.e.the set of target variables of all the possible solutions, from areas ofthe target domain which would be more favorable but are not accessible.The Pareto front is adjoined by further hypersurface portions boundingthe target domain, which contain solutions which although notPareto-optimal under some circumstances are nevertheless of interest.

Suitable for the search for Pareto-optimal solutions are semi-stochasticmethods, which are based for example on the natural process of evolutionof living beings by crossing, mutation and selection and areaccomplished by means of so-called evolutionary algorithms. These areused for iteratively approximating Pareto-optimal solutions on the basisof specific, for example randomly distributed, starting variables for aset of determination variables, in that the determination variables arevaried with each iteration step, for example by recombinations andrandom mutations, and a new set of determination variables is selectedfrom the test variables produced in this way, by selection based on thecorresponding target variables. As soon as a specific terminatingcriterion is satisfied, the iteration is terminated.

Represented in FIG. 4 is a situation in which the determination domainis 3-dimensional, with parameters x₁, x₂ and x₃. The determination set Bover which the determination variable is varied is restricted by thevariables respectively lying between zero and an upper limit X₁, X₂ andX₃, respectively, and therefore forms a cuboid, the product of theintervals [0,X₁], [0,X₂] and [0,X₃]. By means of a known functionalrelationship f, which may be provided by a mathematical model or by atest setup, each determination variable x=(x₁,x₂,x₃) is assigned atarget variable y=f(x), which lies in a target set Z. It is a subset ofthe in this case 2-dimensional target domain, i.e. y=(y₁, y₂), where y₁and y₂ represent two characteristics which are to be optimized. Thetarget set Z may be the complete image set of the determination set Bunder the mapping f or part of the same restricted by constraints.

The target variables of the solutions sought form a so-called Paretofront P (solid line), which bounds the target set Z with respect tosmall, i.e. favorable, values of the characteristics y₁, y₂. Laterallyadjoining the Pareto front P are solutions which likewise lie on theborder of the target set Z. They are not Pareto-optimal, since for eachof the solutions a solution in which both characteristics are morefavorable can be found on the Pareto front, but under some circumstancesthey may likewise be of interest.

It is then primarily a matter of finding determination variables x withwhich the associated target variables y=f(x) lie as close as possible tothe Pareto front P. They are also to be distributed with some degree ofuniformity over the entire Pareto front P and as far as possible alsoover the border areas adjoining the latter of the target set Z.Solutions of this type are generated by means of an iterativeevolutionary or genetic algorithm, which forms the basis of a programwhich runs on a data-processing system. In this case, generally eachvariable is coded by a bit vector of a length L, which is for example32.

For finding approximately Pareto-optimal solutions, firstly startingvariables lying in the determination set B which, as the first set ofdetermination variables, form the starting point of the iteration aregenerated. They may, for example, be distributed regularly or randomlyover the determination set B. Then, as many iteration steps as it takesto satisfy a terminating criterion are carried out. This criterion maybe that a specific maximum number of iteration steps has been carriedout or a specific computing time has elapsed or else that the changingof the target variables has remained below a specific minimum during aspecific number of iteration steps.

With each iteration step, the following substeps are carried out:

Recombination: new variables are respectively generated by combinationof parts of a number of determination variables from the present set.For example, firstly either all the possible ordered pairs ofdetermination variables are formed or else only some of those determinedby means of a random generator. Each determination variable forms avector comprising n real parameters. Then, a number l is likewisegenerated by means of a random generator, where 0≦l≦n, and then two newvariables are formed in that the first l parameters are taken from thefirst determination variable and the remainder are taken from the seconddetermination variable, and vice versa.

Mutation: for the variables generated in the recombination step,variables generated by means of a random generator, for example on thebasis of a normal distribution, are added. Of course it is also possiblein such a way to generate a number of starting variables from onevariable.

Selection: the two steps mentioned above produce a set of test variableswhich is generally greater than the original set of determinationvariables. From this usually relatively large set of test variables, anew set of determination variables which, on average, are particularlyfavorable is then selected. The procedure for the selection is of greatsignificance for the development of the iteration. To control theapproximation to the Pareto front P and two adjacent areas of the borderof the target set Z, especially to achieve a broad approximation, thefollowing procedure is preferably adopted:

In a first selection step, the hyperplane, identified by the conditiony₁=0, of part of the target domain which comprises the target set Z andwhich in the 2-dimensional case represented (FIG. 5 a) coincides withthe y₂ axis, is subjected to a partition into subsets, which in thiscase form intervals I₁ ^(i). Starting from this basis, the said part ofthe target domain is subdivided into subsets W₁ ^(i), which are theoriginal images of the orthogonal projections of the same along thepositive y₁ axis onto the said intervals I₁ ^(i). To put it another way,the subset W₁ ^(i) for a specific i is the set of all points y=(y₁,y₂)in the said part of the target domain for which y₁>0 and y₂ lies in I₁^(i). In FIG. 5 a, it forms a strip parallel to the coordinate axis y₁.

For each of the non-overlapping subsets W₁ ^(i), that test variable forwhich y₁ is optimal, i.e. minimal, is then determined and selected. InFIG. 5 a, the target variables of all the test variables are marked by acircle O, those of the test variables selected in the individual W₁ ^(i)are identified by a superposed multiplication symbol x.

In a second selection step, the part of the target domain containing thetarget set Z is subdivided in an entirely analogous way into subsets W₂^(j) and there, too, again for each subset that test variable for whichy₂ is optimal, i.e. minimal, is determined and selected. The solutionsare identified in FIG. 5 b by a superposed plus symbol +. The new set ofdetermination variables, with which the next iteration step is thenundertaken, are composed of the test variables selected in bothselection steps.

In relatively many cases, in particular in the proximity of the middlearea of the Pareto front P, it is the same test variables that aredetermined in both cases, so that one selection step is usually adequateto establish these test variables. In the lateral border areas, and inparticular in the part of the border of the target set Z adjoining thePareto front P, this is generally not the case, however. If importanceis also attached to the determination of solutions in these areas, it isnecessary to carry out both selection steps.

There is of course also the possibility of respectively selecting ineach of the subsets not just a test variable but a selection set of testvariables, for example the k most favorable with regard to the remainingcomponent, where k>1.

The procedure described for the selection can easily be transferred tocases in which the dimension m of the target domain is greater than 2.In this case, preferably all m hyperplanes which are characterized inthat one of the coordinates y₁, . . . , y_(m) is equal to zero will beformed and a partition of the same into subsets carried out in eachcase. This can take place by each of the coordinate axes beingsubdivided into intervals from the outset and all the products ofintervals into which the coordinate axes spanning the hyperplane aresubdivided then respectively being used as subsets of a hyperplane.

In each of the subsets which are formed by the original images of theorthogonal projections onto the subsets of the hyperplanes, the testvariable most favorable with respect to the remaining component is thenselected and, finally, the union of the selected test variables isformed over the subsets and hyperplanes to produce the new set ofdetermination variables. Depending on whether a determination ofsolutions that is as comprehensive as possible is of interest or, inparticular, it is wished to establish solutions lying in specific areas,the selection may also consider only some of the hyperplanes, especiallysince, as explained above in the example, the central areas of thePareto front are usually already covered quite well in the firstselection step.

The actual procedure, determined by the algorithm, may of course deviatefrom that described above by a different combination of individual stepsetc., in particular it is not absolutely necessary for the selectionsteps described to be carried out one after the other.

The subdivision into intervals may in each case be scaled uniformly orlogarithmically, but may also be finer for instance in areas in whichthere is a particular interest. The partitions into subsets may bemaintained or changed during the overall iteration, for example adaptedto the distribution of the target variables. Instead of or in additionto hyperplanes, subdomains of a smaller dimension may also be used, butthen optimization has to be carried out in each subset with respect to anumber of characteristics, which requires further stipulations or arecursive procedure.

For instance, a wide variety of modifications of the procedure describedare conceivable for the selection. The procedure described has theadvantage that the stipulations regarding the position of the targetvariables allow the determination of the solutions to be respectivelycontrolled in such a way that the target variables derived from the sameare finally distributed in a desired way over a border area of thetarget set. Of course, various modifications are also possible for therecombination and the mutation. These substeps are also not bothrequired in every case.

In the case of the present optimization problem, the determinationdomain is defined by the distributing parameters p₁, . . . , p₇, whichmay respectively vary over the interval [0,1], the target domain, on theother hand, is defined by emissions and pulsations, in the example thetwo characteristics NO_(x) content and maximum amplitude A of thepressure waves occurring. The target domain is represented in FIGS. 6 a,6 b, to be precise with the target variables of the 100 solutionsdetermined after 20 iteration steps (FIG. 6 a) and the 320 solutionsdetermined after 64 iteration steps (FIG. 6 b). The two mappings clearlyshow how more and more, in particular favorable, solutions aredetermined and the limit of the set of target variables graduallyemerges toward the favorable values of the characteristics—the Paretofront.

From the solutions determined, a specific solution is then selected, itbeing possible for further, possibly rather more intuitive, criteria tobe included in the decision. The determination variable of the selectedsolution is then taken as a basis for the production of the burnersystem, especially the production or setting of the distributing device5. Consequently, a burner system in which the distributing parametersp₁, . . . , p₇, and consequently the mass flows m₁, . . . , m₈, havebeen fixed such that they correspond to the determination variable ofthe selected solution is produced.

If, in addition to the control valves, the distributing device 5 alsocontains on/off valves, as represented in FIG. 1, the determinationdomain must be supplemented by corresponding binary switchingparameters, which are respectively represented by a bit which can assumethe values 0 for ‘closed’ and 1 for ‘open’. The occurrence of theseparameters changes virtually nothing concerning the way in which theoptimization proceeds as described further above. A change is necessaryonly in the case of the mutation. Here it may be provided, for example,that each switching parameter, that is each bit, is inverted with aspecific, for example fixed, probability, that is 0 changes into 1 and 1changes into 0.

FIG. 7 shows as examples five different solutions, i.e. mass flowdistributions, the x-axis showing the numbers of the control valves V₁,. . . , V₈ and the y-axis showing the mass flows m₁, . . . , m₈. Thecharacteristics thereby achieved can be taken from the following table:

maximum NO_(x) content amplitude Solution Symbol [ppm] [mbar] 1 Circle2.5 3.12 (equipartition) 2 Rhombus 3.0 2.92 3 Triangle 4.0 2.83 4 Cross5.0 2.80 5 Square 2.0 3.37Solutions 3 and 4 offer particularly favorable values as far as thepressure surges occurring are concerned, while solution 5 shows the bestexhaust gas values, although with high values for the pressure maximum.Solution 2, on the other hand, again offers very good characteristics inthis respect, for which only a slightly increased NO_(x) emission has tobe accepted.

Of course, various deviations from the example described are possible.For instance, additional characteristics or different characteristicsthan those described, such as for example CO emission, average amplitudeof the sound generated, and the like, can be taken as a basis for theoptimization. The optimization method may also deviate from thatdescribed. It is also possible to carry out the search forPareto-optimal solutions for different loads and corresponding values ofthe total mass flow M, and consequently to determine solutions whichhave favorable characteristics over a greater working range.

Finally, the solution which best meets the requirements is selected anda burner system in which the premix burners corresponding to that usedin the test setup respectively have a fixed axial mass flow distributionwhich corresponds to the determination variable of the selected solutionis produced. The setting of the desired mass flow distribution can inthis case be performed in various ways. For example, distributingdevices with restrictors or diverters which produce the desired fixedmass flow distribution in a way which is as simple and reliable aspossible may be used in the burner system. The mass flow distributionmay, however, also be set very simply by the dimensioning, especiallythe diameters of the inlet openings. In this case, the distributingdevice may be in each case comprise a pipe system which connects itsinput to the inlet openings.

List of designations  1 premix burner  2 opening  3a, b air inlet slots 4 inlet openings  5 distributing device  6 main line  7 input valve  8branch lines  9 data-processing system 10 control unit 11 measuring unit

1. A method for producing a burner system having a fuel source, at leastone swirl-stabilized premix burner having a plurality of inlet openings,and a distributing device by which the plurality of inlet openings inthe premix burner are connected to the fuel source, the methodcomprising: determining a desired mass flow distribution into the atleast one premix burner; generating determination variables fixing themass flow distribution, comprising vectors from a determination setwhich is a subset of an n-dimensional domain, one after the other with adata-processing system; setting a mass flow distribution in a test setupwith at least one premix burner and at least one adjustable distributingdevice, the mass flow distribution being set based on each determinationvariable, and measuring a target variable, comprising a vector from atarget set which is a subset of an m-dimensional domain, on the testsetup; and selecting a determination variable on the basis of the targetvariables; wherein the at least one premix burner or the at least onedistributing device of the burner system is configured such that themass flow distribution corresponds to that which is fixed by theselected determination variable.
 2. The method as claimed in claim 1,further comprising: forming the components of the determinationvariables at least partly by the distributing parameters of thebranching points of a tree structure, by which tree structure thedistribution of the mass flow between inlet openings or groups of inletopenings of the at least one premix burner is determined.
 3. The methodas claimed in claim 1, further comprising: determining Pareto solutions,wherein for every solution in which one component of the target variablehas a more favorable value, at least one other component has a lessfavorable value, at least approximately with the data-processing system;and selecting a determination variable from among the Pareto solutions.4. The method as claimed in claim 3, wherein determining Paretosolutions comprises determining starting variables serving as a set ofdetermination variables; and further comprising: carrying out iterationsteps with the data processing system until a terminating criterion issatisfied including determining a new set of determination variablesfrom a set of determination variables by generating from the set ofdetermination variables a set of test variables respectively lying inthe determination set, from which set of test variables the new set ofdetermination variables is selected in each case on the basis of thetarget variables which were measured for the mass flow distributionfixed by the determination variables.
 5. The method as claimed in claim4, wherein generation of the test variables from the set ofdetermination variables comprises random mutation or recombination ofthe determination variables using the data-processing system.
 6. Themethod as claimed in claim 1, wherein the concentration of at least onepollutant forms a component of the target variable.
 7. The method asclaimed in claim 1, wherein a measure of the pulsations occurring in theburner system forms a component of the target variable.
 8. The method asclaimed in claim 1, wherein the inlet openings are provided at leastpartly axially in succession.
 9. The method as claimed in claim 1,further comprising: dimensioning the inlet openings at least partiallyto achieve the desired mass flow distribution.
 10. The method as claimedin claim 1, wherein the distributing device comprises restrictors,diverters, or both, to achieve the desired mass flow distribution. 11.The method as claimed in claim 6, wherein the at least one pollutantcomprises NOx concentration in an exhaust gas.
 12. The method as claimedin claim 7, wherein the measure of the pulsations comprises pulsationmaximum amplitude.
 13. The method as claimed in claim 4, whereinselection of the new set of determination variables from the set of testvariables involves determining a plurality of coordinates of the targetset, and for each of them dividing the hyperplane characterized by thecoordinate equaling zero into subsets, selecting, in the inverse imageof each subset under orthogonal projection parallel to the saidcoordinate, according to the value of its component which corresponds tothe said coordinate, at least one target variable which was measured forthe mass flow distribution fixed by a test variable, and adding the atleast one test variable to the new set of determination variables.